Distributed acoustic sensing (DAS) technology has been applied in vertical seismic profiling (VSP) to provide high-precision seismic records for oil and gas exploration in recent years. However, these records are often contaminated by various noises due to the limitations of acquisition instrument and borehole environment, resulting in an unclear reflection of the geologic structure. The object of this article is to remove the severe noise contamination through signal decomposition, time-frequency description, and feature classification. First, we design an improved decomposition method to avoid signal distortion and reduce frequency aliasing during the decomposition process. A flexible sifting iterative stop condition is applied to sift through unevenly distributed noises in the DAS records. Second, we construct a high-dimensional attribute space using time and frequency factors to represent the confused decomposed components. This ascending-dimensional feature mapping facilitates the description of differences between seismic signal and noise. Finally, we establish a two-level ensemble framework based on tree-structure learners to complete the classification tasks in the high-dimensional feature space. Experiments have proved that this method effectively recovers seismic signal waves while accurately suppressing the complex noises. The improved decomposition method overcomes signal distortion and reduces frequency aliasing, providing clearer information for feature extraction. The tree-structure ensemble model exhibits high accuracy and strong generalization, ensuring the low-attenuation recovery of effective signals. Furthermore, this research reveals the mechanism by which noise interferes with signals and identifies the dominant frequency of random noise.

中文翻译：

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钻孔地震数据多维信噪特征分离的改进本征时标分析


近年来，分布式声学传感（DAS）技术已应用于垂直地震剖面（VSP），为油气勘探提供高精度地震记录。然而，由于采集仪器和井眼环境的限制，这些记录往往受到各种噪声的污染，导致地质结构反映不清晰。本文的目的是通过信号分解、时频描述和特征分类来消除严重的噪声污染。首先，我们设计了一种改进的分解方法，以避免分解过程中的信号失真并减少频率混叠。应用灵活的筛选迭代停止条件来筛选 DAS 记录中分布不均匀的噪声。其次，我们使用时间和频率因子构建高维属性空间来表示混淆的分解成分。这种升维特征映射有助于描述地震信号和噪声之间的差异。最后，我们建立了基于树结构学习器的两级集成框架，以完成高维特征空间中的分类任务。实验证明，该方法能够有效地恢复地震信号波，同时准确地抑制复杂噪声。改进的分解方法克服了信号失真并减少了频率混叠，为特征提取提供了更清晰的信息。树结构集成模型精度高、泛化能力强，保证了有效信号的低衰减恢复。此外，这项研究揭示了噪声干扰信号的机制，并识别了随机噪声的主频率。